In the run up to the Digital Manufacturing Strategies Summit USA HiveMQ have supplied some interested to read articles and content that might raise your interest.
Whitepaper: Building a scalable data foundation for real-time operational intelligence
Manufacturers don’t have a data problem; they have an architecture problem. Operational information already exists across machines, control systems, MES, historians, quality platforms, maintenance systems and enterprise applications, but it is rarely connected, contextualized, analyzed and operationalized through one coherent architecture.
This white paper presents a four-stage approach, Connect, Contextualize, Analyze, Act, that creates immediate value through real-time visibility, standardized KPIs, operational analytics, quality monitoring and decision support, while establishing the event-driven and semantic foundation required to safely delegate selected, clearly governed tasks to AI agents over time.
Who is this whitepaper for: For CIOs & CTOs setting enterprise data and AI strategy, for operations & engineering leaders improving throughput, quality and uptime, for IT / OT Integration Architects connecting plant floor to enterprise, for manufacturing IT & Quality teams standardizing KPIs and governance, for Data & Analytics Teams building trusted, reusable data products, and for Industrial AI Architects delivering governed, agentic use cases.
Whitepaper: Closing The Industrial AI Value Gap
Most industrial AI initiatives fail not because the technology is unready, but because the data foundation beneath it is broken. This white paper makes the case that the primary determinant of industrial AI success is the ability to connect operational data reliably, contextualize it with consistent structure and governance, and act on it in real time with appropriate controls. MQTT and event-driven data streaming are the backbone of that foundation.
Who is this Whitepaper for: CIOs, CTOs, Operations and Engineering Leaders, Industrial AI Architects, IT/OT Integration Strategists, and Business Leaders accountable for AI ROI.
Report: Accelerating Industrial AI in 2026: The Report
https://www.hivemq.com/resources/the-report-accelerating-industrial-ai-in-2026/Industrial teams are eager to apply AI, but the survey shows readiness still lags ambition, largely because organizations lack a real-time, contextualized, governed data foundation.
What Respondents are Prioritizing for 2026
Top focus areas include agentic AI and autonomous operations (67%), edge computing and real-time data processing (63%), digital twins and simulation (51%), and data governance/contextualization tools (36%).
Adoption is Still Early-Stage
Only 7% say AI is embedded in most core processes. Many are still in pilots/POCs (36%) or research with no concrete plans (32%).
The Biggest Barrier: Data, not Models
Respondents cite data quality/availability and legacy integration + silos as key challenges. Just 34% report production systems with real-time data streaming today.
The 2026 Playbook
The report recommends: improving data quality and governance, strengthening IT/OT collaboration, expanding real-time data streaming, modernizing brittle integrations, and tracking ROI with clear KPIs to scale what works.
If you are looking to get hard data on what industrial teams are actually prioritizing for 2026, such as agentic AI, edge + real-time processing, digital twins, governance, and where adoption really stands today, download this report now!
The report also helps you avoid common “AI pilot purgatory” mistakes by pinpointing the biggest blockers (data quality/availability, legacy integration, silos, lack of real-time streaming) and outlining practical recommendations to build readiness and prove ROI. Watch our webinar, Accelerating Industrial AI in 2026, for more insights. Read more here >>
Blog: Governance: Why your UNS won't fail at the first site but will at the second
A unified namespace almost always works at the first site. Governance, not MQTT, is what decides whether it holds up at the second site and beyond.
- UNS failure shows up at the second site, when naming, units, and payload structures start to diverge between sites.
- In energy, governance has to live at the edge, in-stream, because by the time bad data reaches a warehouse, the operational window has passed.
- Active governance, not a static standard document, is what catches deviations before they corrupt a forecast, a dispatch decision, or an AI recommendation.
Who this blog is for: CDOs, CTOs, VPs of digital, and transformation leaders deciding where to invest as their UNS program scales beyond a single site, and what to stop funding.
Blog: Five questions to ask before choosing an industrial data platform
A reliable data streaming foundation is table stakes, not a shortfall. These five questions help you see clearly what it takes to turn that foundation into real-time insight and governed action, whether you’re evaluating a new vendor or building on the one you already trust.
- Streaming reliably moves data. Contextualizing, analyzing and acting on it in real time is the next capability layer, and it’s worth knowing what that requires before committing to a roadmap.
- The real cost of skipping that layer shows up at site two, not site one, when replication should get easier and instead gets harder.
- An architecture that can’t govern data close to where it’s created will need a second system added before any AI initiative can act on it with confidence.
Who this blog is for: Transformation and digital leaders scoping or renewing a data infrastructure investment for AI-ready operations, including teams building on an established streaming foundation.
Blog: Foundational UNS patterns for agent-ready manufacturing
A well-designed Unified Namespace provides the architectural foundation that makes AI agent deployment in manufacturing possible without costly rework.
- Context-first design, where every data point carries semantic meaning from the moment it enters the namespace, eliminates the integration debt that blocks intelligent automation.
- Four foundational UNS patterns (hierarchical topic design, semantic enrichment, event-driven state management, and governed access) determine whether your architecture is agent-ready or agent-hostile.
- Organizations that treat UNS as “just an integration layer” spend 3-5x more on retrofitting when they later pursue AI-driven operations.
Who this blog is for: IT/OT solutions architects and data architects in manufacturing who are designing or evaluating a Unified Namespace and need to know whether their architecture will support AI agents later, or require costly rework.
Blog: De-mystifying MQTT vs. OPC UA: What the comparisons get wrong
Recent MQTT vs. OPC UA comparisons repeat a handful of claims about deterministic, bidirectional feedback loops that don’t hold up under technical scrutiny. This post corrects the record, shows the MQTT 5 properties and topic patterns that handle the cases critics point to, and closes with a practical starting checklist.
- A new MQTT topic doesn’t require touching a server’s information model the way an OPC UA address space change often does. That freedom still has to live inside a governed namespace, not outside one.
- MQTT is push-based. Delays shown in side-by-side demos usually come from application logic, not the protocol, and MQTT 5’s request/response properties handle deterministic feedback loops natively.
- OPC UA’s client-server model fits orchestration. MQTT’s publish/subscribe model fits choreography. Neither is a downgrade of the other; they solve different coordination problems.
Who this blog is for: OT/IT architects and engineering leads who keep running into MQTT vs. OPC UA comparisons that don’t match what the protocols actually do, and who want working topic patterns and configuration guidance, not just a technically precise argument.
